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SAMPLING METHODS

SAMPLING METHODS. Chapter 5. LEARNING OBJECTIVES. Reasons for sampling Different sampling methods Probability & non probability sampling Advantages & disadvantages of each sampling method. SAMPLING. A sample is a smaller collection of units from a population

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SAMPLING METHODS

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  1. SAMPLING METHODS Chapter 5

  2. LEARNING OBJECTIVES • Reasons for sampling • Different sampling methods • Probability & non probability sampling • Advantages & disadvantages of each sampling method

  3. SAMPLING • A sample is a smaller collection of units from a population • Used to learn about that population

  4. SAMPLING • Why sample? • Saves Resources • Time • Money • Workload

  5. SAMPLING FRAME • The list from which the potential respondents are drawn • Registrar’s office • Class rosters • Elements=Members of population whose characteristics are measured

  6. Population • What is your population of interest? • To whom do you want to generalize your results? • All doctors • School children • Indians • Women aged 15-45 years • Other…

  7. SAMPLING • 3 factors that influence sample representativeness 1. Sampling procedure 2. Sample size 3. Participation (response)

  8. SAMPLING • When might you sample the entire population? • Population is very small • You have extensive resources • Don’t expect a very high response

  9. SAMPLING BREAKDOWN

  10. SAMPLING STUDY POPULATION SAMPLE TARGET POPULATION

  11. SAMPLING

  12. PROBABILITY SAMPLING • Every unit in population has a chance (greater than zero) of being selected into sample • Probability of being selected can be determined • Every element in population has same probability of selection= ‘Equal Probability of Selection'(EPS) design

  13. PROBABILITY SAMPLING INCLULDES • 1. Simple Random Sampling • 2. Systematic Sampling • 3. Stratified Random Sampling • 4. Cluster Sampling • https://www.youtube.com/watch?v=be9e-Q-jC-0

  14. 1. SIMPLE RANDOM SAMPLING • When population is: • Small • Homogeneous • Readily available • Each element of the frame has equal probability of selection • Provides for greatest number of possible samples. • Assigning number to each unit in sampling frame • A table of random numbers or lottery system is used to determine which units are selected

  15. SIMPLE RANDOM SAMPLING • Disadvantages • If sampling frame is large, method impractical • Minority subgroups of interest in population may not be present in sample in sufficient numbers for study

  16. 2/11 2. SYSTEMATIC SAMPLING • Elements of population are put in a list • Then every kth element in list is chosen (systematically) for inclusion in sample • For example, if population of study contained 2,000 students at a high school and the researcher wanted a sample of 100 students,

  17. SYSTEMATIC SAMPLING • Students are put in a list • Then every 20th student is selected for inclusion in sample • To ensure against human bias: • Researcher should select first individual at random. • ‘Systematic sample with a Random start'

  18. SYSTEMATIC SAMPLING EPS method, because all elements have the same probability of selection (In the example, 1 in 20)

  19. Another Example • A researcher wants to select a systematic random sample of 10 people from a population of 100. If he or she has a list of all 100 people, he would assign each person a number from 1 to 100. • Researcher then picks a random number, 6, as the starting number. • He or she would then select every tenth person for the sample (because the sampling interval = 100/10 = 10). • The final sample would contain those individuals who were assigned the following numbers: 6, 16, 26, 36, 46, 56, 66, 76, 86, 96.

  20. SYSTEMATIC SAMPLING • ADVANTAGES: • Simple • Guaranteed that population will be evenly sampled • DISADVANTAGE: • Sample may be biased if hidden periodicity in population coincides with that of selection.

  21. 3. STRATIFIED SAMPLING • Population contains a number of categories • Sampling frame can be organized into separate "strata“ • Each stratum is sampled as an independent sub-population • Every unit in a stratum has same chance of being selected.

  22. STRATIFIED SAMPLING Draw a sample from each stratum

  23. STRATIFIED SAMPLING

  24. STRATIFIED SAMPLING • Benefits: • Using same sampling fraction for all strata ensures proportionate representation in sample • Adequate representation of minority subgroups of interest can be ensured by stratification • Drawbacks: • Sampling frame of entire population has to be prepared separately for each stratum • In some cases (designs with a large number of strata, or with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than other methods

  25. 4. Cluster Sampling • http://www.youtube.com/watch?v=QOxXy-I6ogs • Advantage of cluster sampling: • Cheap • Quick • Easy 1. Researcher can allocate resources to a few randomly selected clusters 2. Researcher can have a larger sample size than using simple random sampling. Take one sample from a number of clusters

  26. Non-Probability Samples 1. Convenience sample 2. Quota 3. Purposive sample

  27. NON PROBABILITY SAMPLING • Any sampling method where some elements of population have no chance of selection or • Where probability of selection cannot be accurately determined • Selection of elements based on assumptions regarding population of interest

  28. NON PROBABILITY SAMPLING • Example: Visit every household in a given street, and • Interview the first person to answer the door. • In any household with more than one occupant, this is a nonprobability sample, • Some people are more likely to answer the door (e.g. an unemployed person vs employed housemate)

  29. NONPROBABILITY SAMPLING • Nonprobability Sampling includes: Convenience Sampling,Quota Sampling and Purposive Sampling. • In addition, non-response effects may turn any probability design into a nonprobability design if the characteristics of nonresponse are not well understood • Non-response effectively modifies each element's probability of being sampled

  30. 1. CONVENIENCE SAMPLING Use results that are easy to get 30

  31. CONVENIENCE SAMPLING • Sometimes known as: • Grab • Opportunity • Accidental or • Haphazard sampling • Nonprobability sampling which involves sample drawn from part of population that is close. • That is, readily available=Convenient • Researcher cannot scientifically make generalizations about total population from this sample • Sample not representative

  32. CONVENIENCE SAMPLING • Example: Interviewer conducts survey at shopping center early in morning on a given day • People he/she could interview limited to those in shopping center at that time on that day

  33. CONVENIENCE SAMPLING • Sample would not represent views of other people in that area who might be at the mall at different times of day or different days of the week • This type of sampling is most useful for pilot testing.

  34. 2. QUOTA SAMPLING 1. Population is segmented into mutually exclusive sub-groups 2. Judgment used to select subjects or units from each segment based on a specified proportion. 3. For example, an interviewer may be told to sample 200 females and 300 males between ages of 45 and 60. • This step makes the technique non-probability sampling.

  35. QUOTA SAMPLING • In quota sampling, selection of sample is non-random. • For example: Interviewers might be tempted to interview those who look most helpful. • Problem: Samples may be biased because not everyone gets a chance of selection. • Greatest weakness

  36. 3. Purposive Sample • Sample is selected based on researchers’ knowledge of a population and purpose of the study. • Subjects selected because of some characteristic. • Field researchers often interested in studying extreme or deviant cases • Cases that don’t fit into regular patterns of attitudes and behaviors

  37. Purposive Sample • Studying deviant cases, researchers often gain a better understanding of regular patterns of behavior • This is where purposive sampling often takes place. • For instance, if a researcher is interested in learning more about students at the top of their class, • Sample those students who fall into the "top of the class" category. • They will be purposively selected because they meet a certain characteristic.

  38. Purposive Sample • Can be very useful for situations where you need to reach a targeted sample quickly and • Where sampling for proportionality is not the main concern.

  39. Purposive Sample • Example: • Researchers (typically market researchers) who you might often see at a mall carrying a clipboard and stopping various people to interview • Often conducting research using purposive sampling. • May be looking for and stopping only those people who meet certain characteristics.

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